# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# ==============================================================================
"""A powerful dynamic attention wrapper object."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import functools
import math

import numpy as np

import tensorflow as tf
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.layers import base as layers_base
from tensorflow.python.layers import core as layers_core
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import clip_ops
from tensorflow.python.ops import functional_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import rnn_cell_impl
from tensorflow.python.ops import tensor_array_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.util import nest
from tensorflow.python.layers.core import Dense

__all__ = [
    "AttentionMechanism",
    "AttentionWrapper",
    "AttentionWrapperState",
    "LuongAttention",
    "BahdanauAttention",
    "hardmax",
    "safe_cumprod",
    "monotonic_attention",
    "BahdanauMonotonicAttention",
    "LuongMonotonicAttention",
]

_zero_state_tensors = rnn_cell_impl._zero_state_tensors  # pylint: disable=protected-access


class AttentionMechanism(object):
    pass


def _prepare_memory(memory, memory_sequence_length, check_inner_dims_defined):
    """Convert to tensor and possibly mask `memory`.

    Args:
      memory: `Tensor`, shaped `[batch_size, max_time, ...]`.
      memory_sequence_length: `int32` `Tensor`, shaped `[batch_size]`.
      check_inner_dims_defined: Python boolean.  If `True`, the `memory`
        argument's shape is checked to ensure all but the two outermost
        dimensions are fully defined.

    Returns:
      A (possibly masked), checked, new `memory`.

    Raises:
      ValueError: If `check_inner_dims_defined` is `True` and not
        `memory.shape[2:].is_fully_defined()`.
    """
    memory = nest.map_structure(
        lambda m: ops.convert_to_tensor(m, name="memory"), memory)
    if memory_sequence_length is not None:
        memory_sequence_length = ops.convert_to_tensor(
            memory_sequence_length, name="memory_sequence_length")
    if check_inner_dims_defined:
        def _check_dims(m):
            if not m.get_shape()[2:].is_fully_defined():
                raise ValueError("Expected memory %s to have fully defined inner dims, "
                                 "but saw shape: %s" % (m.name, m.get_shape()))

        nest.map_structure(_check_dims, memory)
    if memory_sequence_length is None:
        seq_len_mask = None
    else:
        seq_len_mask = array_ops.sequence_mask(
            memory_sequence_length,
            maxlen=array_ops.shape(nest.flatten(memory)[0])[1],
            dtype=nest.flatten(memory)[0].dtype)
        seq_len_batch_size = (
                memory_sequence_length.shape[0].value
                or array_ops.shape(memory_sequence_length)[0])

    def _maybe_mask(m, seq_len_mask):
        rank = m.get_shape().ndims
        rank = rank if rank is not None else array_ops.rank(m)
        extra_ones = array_ops.ones(rank - 2, dtype=dtypes.int32)
        m_batch_size = m.shape[0].value or array_ops.shape(m)[0]
        if memory_sequence_length is not None:
            message = ("memory_sequence_length and memory tensor batch sizes do not "
                       "match.")
            with ops.control_dependencies([
                check_ops.assert_equal(
                    seq_len_batch_size, m_batch_size, message=message)]):
                seq_len_mask = array_ops.reshape(
                    seq_len_mask,
                    array_ops.concat((array_ops.shape(seq_len_mask), extra_ones), 0))
                return m * seq_len_mask
        else:
            return m

    return nest.map_structure(lambda m: _maybe_mask(m, seq_len_mask), memory)


def _maybe_mask_score(score, memory_sequence_length, score_mask_value):
    if memory_sequence_length is None:
        return score
    message = ("All values in memory_sequence_length must greater than zero.")
    with ops.control_dependencies(
            [check_ops.assert_positive(memory_sequence_length, message=message)]):
        score_mask = array_ops.sequence_mask(
            memory_sequence_length, maxlen=array_ops.shape(score)[1])
        score_mask_values = score_mask_value * array_ops.ones_like(score)
        return array_ops.where(score_mask, score, score_mask_values)


class _BaseAttentionMechanism(AttentionMechanism):
    """A base AttentionMechanism class providing common functionality.

    Common functionality includes:
      1. Storing the query and memory layers.
      2. Preprocessing and storing the memory.
    """

    def __init__(self,
                 query_layer,
                 memory,
                 probability_fn,
                 memory_sequence_length=None,
                 memory_layer=None,
                 check_inner_dims_defined=True,
                 score_mask_value=None,
                 name=None):
        """Construct base AttentionMechanism class.

        Args:
          query_layer: Callable.  Instance of `tf.layers.Layer`.  The layer's depth
            must match the depth of `memory_layer`.  If `query_layer` is not
            provided, the shape of `query` must match that of `memory_layer`.
          memory: The memory to query; usually the output of an RNN encoder.  This
            tensor should be shaped `[batch_size, max_time, ...]`.
          probability_fn: A `callable`.  Converts the score and previous alignments
            to probabilities. Its signature should be:
            `probabilities = probability_fn(score, previous_alignments)`.
          memory_sequence_length (optional): Sequence lengths for the batch entries
            in memory.  If provided, the memory tensor rows are masked with zeros
            for values past the respective sequence lengths.
          memory_layer: Instance of `tf.layers.Layer` (may be None).  The layer's
            depth must match the depth of `query_layer`.
            If `memory_layer` is not provided, the shape of `memory` must match
            that of `query_layer`.
          check_inner_dims_defined: Python boolean.  If `True`, the `memory`
            argument's shape is checked to ensure all but the two outermost
            dimensions are fully defined.
          score_mask_value: (optional): The mask value for score before passing into
            `probability_fn`. The default is -inf. Only used if
            `memory_sequence_length` is not None.
          name: Name to use when creating ops.
        """
        if (query_layer is not None
                and not isinstance(query_layer, layers_base.Layer)):
            raise TypeError(
                "query_layer is not a Layer: %s" % type(query_layer).__name__)
        if (memory_layer is not None
                and not isinstance(memory_layer, layers_base.Layer)):
            raise TypeError(
                "memory_layer is not a Layer: %s" % type(memory_layer).__name__)
        self._query_layer = query_layer
        self._memory_layer = memory_layer
        self.dtype = memory_layer.dtype
        if not callable(probability_fn):
            raise TypeError("probability_fn must be callable, saw type: %s" %
                            type(probability_fn).__name__)
        if score_mask_value is None:
            score_mask_value = dtypes.as_dtype(
                self._memory_layer.dtype).as_numpy_dtype(-np.inf)
        self._probability_fn = lambda score, prev: (  # pylint:disable=g-long-lambda
            probability_fn(
                _maybe_mask_score(score, memory_sequence_length, score_mask_value),
                prev))
        with ops.name_scope(
                name, "BaseAttentionMechanismInit", nest.flatten(memory)):
            self._values = _prepare_memory(
                memory, memory_sequence_length,
                check_inner_dims_defined=check_inner_dims_defined)
            self._keys = (
                self.memory_layer(self._values) if self.memory_layer  # pylint: disable=not-callable
                else self._values)
            self._batch_size = (
                    self._keys.shape[0].value or array_ops.shape(self._keys)[0])
            self._alignments_size = (self._keys.shape[1].value or
                                     array_ops.shape(self._keys)[1])

    @property
    def memory_layer(self):
        return self._memory_layer

    @property
    def query_layer(self):
        return self._query_layer

    @property
    def values(self):
        return self._values

    @property
    def keys(self):
        return self._keys

    @property
    def batch_size(self):
        return self._batch_size

    @property
    def alignments_size(self):
        return self._alignments_size

    def initial_alignments(self, batch_size, dtype):
        """Creates the initial alignment values for the `AttentionWrapper` class.

        This is important for AttentionMechanisms that use the previous alignment
        to calculate the alignment at the next time step (e.g. monotonic attention).

        The default behavior is to return a tensor of all zeros.

        Args:
          batch_size: `int32` scalar, the batch_size.
          dtype: The `dtype`.

        Returns:
          A `dtype` tensor shaped `[batch_size, alignments_size]`
          (`alignments_size` is the values' `max_time`).
        """
        max_time = self._alignments_size
        return _zero_state_tensors(max_time, batch_size, dtype)


def _luong_score(query, keys, scale):
    """Implements Luong-style (multiplicative) scoring function.

    This attention has two forms.  The first is standard Luong attention,
    as described in:

    Minh-Thang Luong, Hieu Pham, Christopher D. Manning.
    "Effective Approaches to Attention-based Neural Machine Translation."
    EMNLP 2015.  https://arxiv.org/abs/1508.04025

    The second is the scaled form inspired partly by the normalized form of
    Bahdanau attention.

    To enable the second form, call this function with `scale=True`.

    Args:
      query: Tensor, shape `[batch_size, num_units]` to compare to keys.
      keys: Processed memory, shape `[batch_size, max_time, num_units]`.
      scale: Whether to apply a scale to the score function.

    Returns:
      A `[batch_size, max_time]` tensor of unnormalized score values.

    Raises:
      ValueError: If `key` and `query` depths do not match.
    """
    depth = query.get_shape()[-1]
    key_units = keys.get_shape()[-1]
    if depth != key_units:
        raise ValueError(
            "Incompatible or unknown inner dimensions between query and keys.  "
            "Query (%s) has units: %s.  Keys (%s) have units: %s.  "
            "Perhaps you need to set num_units to the keys' dimension (%s)?"
            % (query, depth, keys, key_units, key_units))
    dtype = query.dtype

    # Reshape from [batch_size, depth] to [batch_size, 1, depth]
    # for matmul.
    query = array_ops.expand_dims(query, 1)

    # Inner product along the query units dimension.
    # matmul shapes: query is [batch_size, 1, depth] and
    #                keys is [batch_size, max_time, depth].
    # the inner product is asked to **transpose keys' inner shape** to get a
    # batched matmul on:
    #   [batch_size, 1, depth] . [batch_size, depth, max_time]
    # resulting in an output shape of:
    #   [batch_time, 1, max_time].
    # we then squeeze out the center singleton dimension.
    score = math_ops.matmul(query, keys, transpose_b=True)
    score = array_ops.squeeze(score, [1])

    if scale:
        # Scalar used in weight scaling
        g = variable_scope.get_variable(
            "attention_g", dtype=dtype, initializer=1.)
        score = g * score
    return score


class LuongAttention(_BaseAttentionMechanism):
    """Implements Luong-style (multiplicative) attention scoring.

    This attention has two forms.  The first is standard Luong attention,
    as described in:

    Minh-Thang Luong, Hieu Pham, Christopher D. Manning.
    "Effective Approaches to Attention-based Neural Machine Translation."
    EMNLP 2015.  https://arxiv.org/abs/1508.04025

    The second is the scaled form inspired partly by the normalized form of
    Bahdanau attention.

    To enable the second form, construct the object with parameter
    `scale=True`.
    """

    def __init__(self,
                 num_units,
                 memory,
                 memory_sequence_length=None,
                 scale=False,
                 probability_fn=None,
                 score_mask_value=None,
                 dtype=None,
                 name="LuongAttention"):
        """Construct the AttentionMechanism mechanism.

        Args:
          num_units: The depth of the attention mechanism.
          memory: The memory to query; usually the output of an RNN encoder.  This
            tensor should be shaped `[batch_size, max_time, ...]`.
          memory_sequence_length: (optional) Sequence lengths for the batch entries
            in memory.  If provided, the memory tensor rows are masked with zeros
            for values past the respective sequence lengths.
          scale: Python boolean.  Whether to scale the energy term.
          probability_fn: (optional) A `callable`.  Converts the score to
            probabilities.  The default is @{tf.nn.softmax}. Other options include
            @{tf.contrib.seq2seq.hardmax} and @{tf.contrib.sparsemax.sparsemax}.
            Its signature should be: `probabilities = probability_fn(score)`.
          score_mask_value: (optional) The mask value for score before passing into
            `probability_fn`. The default is -inf. Only used if
            `memory_sequence_length` is not None.
          dtype: The data type for the memory layer of the attention mechanism.
          name: Name to use when creating ops.
        """
        # For LuongAttention, we only transform the memory layer; thus
        # num_units **must** match expected the query depth.
        if probability_fn is None:
            probability_fn = nn_ops.softmax
        if dtype is None:
            dtype = dtypes.float32
        wrapped_probability_fn = lambda score, _: probability_fn(score)
        super(LuongAttention, self).__init__(
            query_layer=None,
            memory_layer=layers_core.Dense(
                num_units, name="memory_layer", use_bias=False, dtype=dtype),
            memory=memory,
            probability_fn=wrapped_probability_fn,
            memory_sequence_length=memory_sequence_length,
            score_mask_value=score_mask_value,
            name=name)
        self._num_units = num_units
        self._scale = scale
        self._name = name

    def __call__(self, query, previous_alignments):
        """Score the query based on the keys and values.

        Args:
          query: Tensor of dtype matching `self.values` and shape
            `[batch_size, query_depth]`.
          previous_alignments: Tensor of dtype matching `self.values` and shape
            `[batch_size, alignments_size]`
            (`alignments_size` is memory's `max_time`).

        Returns:
          alignments: Tensor of dtype matching `self.values` and shape
            `[batch_size, alignments_size]` (`alignments_size` is memory's
            `max_time`).
        """
        with variable_scope.variable_scope(None, "luong_attention", [query]):
            score = _luong_score(query, self._keys, self._scale)
        alignments = self._probability_fn(score, previous_alignments)
        return alignments


def _bahdanau_score(processed_query, keys, normalize):
    """Implements Bahdanau-style (additive) scoring function.

    This attention has two forms.  The first is Bhandanau attention,
    as described in:

    Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio.
    "Neural Machine Translation by Jointly Learning to Align and Translate."
    ICLR 2015. https://arxiv.org/abs/1409.0473

    The second is the normalized form.  This form is inspired by the
    weight normalization article:

    Tim Salimans, Diederik P. Kingma.
    "Weight Normalization: A Simple Reparameterization to Accelerate
     Training of Deep Neural Networks."
    https://arxiv.org/abs/1602.07868

    To enable the second form, set `normalize=True`.

    Args:
      processed_query: Tensor, shape `[batch_size, num_units]` to compare to keys.
      keys: Processed memory, shape `[batch_size, max_time, num_units]`.
      normalize: Whether to normalize the score function.

    Returns:
      A `[batch_size, max_time]` tensor of unnormalized score values.
    """
    dtype = processed_query.dtype
    # Get the number of hidden units from the trailing dimension of keys
    num_units = keys.shape[2].value or array_ops.shape(keys)[2]
    # Reshape from [batch_size, ...] to [batch_size, 1, ...] for broadcasting.
    processed_query = array_ops.expand_dims(processed_query, 1)
    v = variable_scope.get_variable(
        "attention_v", [num_units], dtype=dtype)
    if normalize:
        # Scalar used in weight normalization
        g = variable_scope.get_variable(
            "attention_g", dtype=dtype,
            initializer=math.sqrt((1. / num_units)))
        # Bias added prior to the nonlinearity
        b = variable_scope.get_variable(
            "attention_b", [num_units], dtype=dtype,
            initializer=init_ops.zeros_initializer())
        # normed_v = g * v / ||v||
        normed_v = g * v * math_ops.rsqrt(
            math_ops.reduce_sum(math_ops.square(v)))
        return math_ops.reduce_sum(
            normed_v * math_ops.tanh(keys + processed_query + b), [2])
    else:
        return math_ops.reduce_sum(v * math_ops.tanh(keys + processed_query), [2])


class BahdanauAttention(_BaseAttentionMechanism):
    """Implements Bahdanau-style (additive) attention.

    This attention has two forms.  The first is Bahdanau attention,
    as described in:

    Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio.
    "Neural Machine Translation by Jointly Learning to Align and Translate."
    ICLR 2015. https://arxiv.org/abs/1409.0473

    The second is the normalized form.  This form is inspired by the
    weight normalization article:

    Tim Salimans, Diederik P. Kingma.
    "Weight Normalization: A Simple Reparameterization to Accelerate
     Training of Deep Neural Networks."
    https://arxiv.org/abs/1602.07868

    To enable the second form, construct the object with parameter
    `normalize=True`.
    """

    def __init__(self,
                 num_units,
                 memory,
                 memory_sequence_length=None,
                 normalize=False,
                 probability_fn=None,
                 score_mask_value=None,
                 dtype=None,
                 name="BahdanauAttention"):
        """Construct the Attention mechanism.

        Args:
          num_units: The depth of the query mechanism.
          memory: The memory to query; usually the output of an RNN encoder.  This
            tensor should be shaped `[batch_size, max_time, ...]`.
          memory_sequence_length (optional): Sequence lengths for the batch entries
            in memory.  If provided, the memory tensor rows are masked with zeros
            for values past the respective sequence lengths.
          normalize: Python boolean.  Whether to normalize the energy term.
          probability_fn: (optional) A `callable`.  Converts the score to
            probabilities.  The default is @{tf.nn.softmax}. Other options include
            @{tf.contrib.seq2seq.hardmax} and @{tf.contrib.sparsemax.sparsemax}.
            Its signature should be: `probabilities = probability_fn(score)`.
          score_mask_value: (optional): The mask value for score before passing into
            `probability_fn`. The default is -inf. Only used if
            `memory_sequence_length` is not None.
          dtype: The data type for the query and memory layers of the attention
            mechanism.
          name: Name to use when creating ops.
        """
        if probability_fn is None:
            probability_fn = nn_ops.softmax
        if dtype is None:
            dtype = dtypes.float32
        wrapped_probability_fn = lambda score, _: probability_fn(score)
        super(BahdanauAttention, self).__init__(
            query_layer=layers_core.Dense(
                num_units, name="query_layer", use_bias=False, dtype=dtype),
            memory_layer=layers_core.Dense(
                num_units, name="memory_layer", use_bias=False, dtype=dtype),
            memory=memory,
            probability_fn=wrapped_probability_fn,
            memory_sequence_length=memory_sequence_length,
            score_mask_value=score_mask_value,
            name=name)
        self._num_units = num_units
        self._normalize = normalize
        self._name = name

    def __call__(self, query, previous_alignments):
        """Score the query based on the keys and values.

        Args:
          query: Tensor of dtype matching `self.values` and shape
            `[batch_size, query_depth]`.
          previous_alignments: Tensor of dtype matching `self.values` and shape
            `[batch_size, alignments_size]`
            (`alignments_size` is memory's `max_time`).

        Returns:
          alignments: Tensor of dtype matching `self.values` and shape
            `[batch_size, alignments_size]` (`alignments_size` is memory's
            `max_time`).
        """
        with variable_scope.variable_scope(None, "bahdanau_attention", [query]):
            processed_query = self.query_layer(query) if self.query_layer else query
            score = _bahdanau_score(processed_query, self._keys, self._normalize)
        alignments = self._probability_fn(score, previous_alignments)
        return alignments


def safe_cumprod(x, *args, **kwargs):
    """Computes cumprod of x in logspace using cumsum to avoid underflow.

    The cumprod function and its gradient can result in numerical instabilities
    when its argument has very small and/or zero values.  As long as the argument
    is all positive, we can instead compute the cumulative product as
    exp(cumsum(log(x))).  This function can be called identically to tf.cumprod.

    Args:
      x: Tensor to take the cumulative product of.
      *args: Passed on to cumsum; these are identical to those in cumprod.
      **kwargs: Passed on to cumsum; these are identical to those in cumprod.
    Returns:
      Cumulative product of x.
    """
    with ops.name_scope(None, "SafeCumprod", [x]):
        x = ops.convert_to_tensor(x, name="x")
        tiny = np.finfo(x.dtype.as_numpy_dtype).tiny
        return math_ops.exp(math_ops.cumsum(
            math_ops.log(clip_ops.clip_by_value(x, tiny, 1)), *args, **kwargs))


def monotonic_attention(p_choose_i, previous_attention, mode):
    """Compute monotonic attention distribution from choosing probabilities.

    Monotonic attention implies that the input sequence is processed in an
    explicitly left-to-right manner when generating the output sequence.  In
    addition, once an input sequence element is attended to at a given output
    timestep, elements occurring before it cannot be attended to at subsequent
    output timesteps.  This function generates attention distributions according
    to these assumptions.  For more information, see ``Online and Linear-Time
    Attention by Enforcing Monotonic Alignments''.

    Args:
      p_choose_i: Probability of choosing input sequence/memory element i.  Should
        be of shape (batch_size, input_sequence_length), and should all be in the
        range [0, 1].
      previous_attention: The attention distribution from the previous output
        timestep.  Should be of shape (batch_size, input_sequence_length).  For
        the first output timestep, preevious_attention[n] should be [1, 0, 0, ...,
        0] for all n in [0, ... batch_size - 1].
      mode: How to compute the attention distribution.  Must be one of
        'recursive', 'parallel', or 'hard'.
          * 'recursive' uses tf.scan to recursively compute the distribution.
            This is slowest but is exact, general, and does not suffer from
            numerical instabilities.
          * 'parallel' uses parallelized cumulative-sum and cumulative-product
            operations to compute a closed-form solution to the recurrence
            relation defining the attention distribution.  This makes it more
            efficient than 'recursive', but it requires numerical checks which
            make the distribution non-exact.  This can be a problem in particular
            when input_sequence_length is long and/or p_choose_i has entries very
            close to 0 or 1.
          * 'hard' requires that the probabilities in p_choose_i are all either 0
            or 1, and subsequently uses a more efficient and exact solution.

    Returns:
      A tensor of shape (batch_size, input_sequence_length) representing the
      attention distributions for each sequence in the batch.

    Raises:
      ValueError: mode is not one of 'recursive', 'parallel', 'hard'.
    """
    # Force things to be tensors
    p_choose_i = ops.convert_to_tensor(p_choose_i, name="p_choose_i")
    previous_attention = ops.convert_to_tensor(
        previous_attention, name="previous_attention")
    if mode == "recursive":
        # Use .shape[0].value when it's not None, or fall back on symbolic shape
        batch_size = p_choose_i.shape[0].value or array_ops.shape(p_choose_i)[0]
        # Compute [1, 1 - p_choose_i[0], 1 - p_choose_i[1], ..., 1 - p_choose_i[-2]]
        shifted_1mp_choose_i = array_ops.concat(
            [array_ops.ones((batch_size, 1)), 1 - p_choose_i[:, :-1]], 1)
        # Compute attention distribution recursively as
        # q[i] = (1 - p_choose_i[i])*q[i - 1] + previous_attention[i]
        # attention[i] = p_choose_i[i]*q[i]
        attention = p_choose_i * array_ops.transpose(functional_ops.scan(
            # Need to use reshape to remind TF of the shape between loop iterations
            lambda x, yz: array_ops.reshape(yz[0] * x + yz[1], (batch_size,)),
            # Loop variables yz[0] and yz[1]
            [array_ops.transpose(shifted_1mp_choose_i),
             array_ops.transpose(previous_attention)],
            # Initial value of x is just zeros
            array_ops.zeros((batch_size,))))
    elif mode == "parallel":
        # safe_cumprod computes cumprod in logspace with numeric checks
        cumprod_1mp_choose_i = safe_cumprod(1 - p_choose_i, axis=1, exclusive=True)
        # Compute recurrence relation solution
        attention = p_choose_i * cumprod_1mp_choose_i * math_ops.cumsum(
            previous_attention /
            # Clip cumprod_1mp to avoid divide-by-zero
            clip_ops.clip_by_value(cumprod_1mp_choose_i, 1e-10, 1.), axis=1)
    elif mode == "hard":
        # Remove any probabilities before the index chosen last time step
        p_choose_i *= math_ops.cumsum(previous_attention, axis=1)
        # Now, use exclusive cumprod to remove probabilities after the first
        # chosen index, like so:
        # p_choose_i = [0, 0, 0, 1, 1, 0, 1, 1]
        # cumprod(1 - p_choose_i, exclusive=True) = [1, 1, 1, 1, 0, 0, 0, 0]
        # Product of above: [0, 0, 0, 1, 0, 0, 0, 0]
        attention = p_choose_i * math_ops.cumprod(
            1 - p_choose_i, axis=1, exclusive=True)
    else:
        raise ValueError("mode must be 'recursive', 'parallel', or 'hard'.")
    return attention


def _monotonic_probability_fn(score, previous_alignments, sigmoid_noise, mode,
                              seed=None):
    """Attention probability function for monotonic attention.

    Takes in unnormalized attention scores, adds pre-sigmoid noise to encourage
    the model to make discrete attention decisions, passes them through a sigmoid
    to obtain "choosing" probabilities, and then calls monotonic_attention to
    obtain the attention distribution.  For more information, see

    Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck,
    "Online and Linear-Time Attention by Enforcing Monotonic Alignments."
    ICML 2017.  https://arxiv.org/abs/1704.00784

    Args:
      score: Unnormalized attention scores, shape `[batch_size, alignments_size]`
      previous_alignments: Previous attention distribution, shape
        `[batch_size, alignments_size]`
      sigmoid_noise: Standard deviation of pre-sigmoid noise.  Setting this larger
        than 0 will encourage the model to produce large attention scores,
        effectively making the choosing probabilities discrete and the resulting
        attention distribution one-hot.  It should be set to 0 at test-time, and
        when hard attention is not desired.
      mode: How to compute the attention distribution.  Must be one of
        'recursive', 'parallel', or 'hard'.  See the docstring for
        `tf.contrib.seq2seq.monotonic_attention` for more information.
      seed: (optional) Random seed for pre-sigmoid noise.

    Returns:
      A `[batch_size, alignments_size]`-shape tensor corresponding to the
      resulting attention distribution.
    """
    # Optionally add pre-sigmoid noise to the scores
    if sigmoid_noise > 0:
        noise = random_ops.random_normal(array_ops.shape(score), dtype=score.dtype,
                                         seed=seed)
        score += sigmoid_noise * noise
    # Compute "choosing" probabilities from the attention scores
    if mode == "hard":
        # When mode is hard, use a hard sigmoid
        p_choose_i = math_ops.cast(score > 0, score.dtype)
    else:
        p_choose_i = math_ops.sigmoid(score)
    # Convert from choosing probabilities to attention distribution
    return monotonic_attention(p_choose_i, previous_alignments, mode)


class _BaseMonotonicAttentionMechanism(_BaseAttentionMechanism):
    """Base attention mechanism for monotonic attention.

    Simply overrides the initial_alignments function to provide a dirac
    distribution,which is needed in order for the monotonic attention
    distributions to have the correct behavior.
    """

    def initial_alignments(self, batch_size, dtype):
        """Creates the initial alignment values for the monotonic attentions.

        Initializes to dirac distributions, i.e. [1, 0, 0, ...memory length..., 0]
        for all entries in the batch.

        Args:
          batch_size: `int32` scalar, the batch_size.
          dtype: The `dtype`.

        Returns:
          A `dtype` tensor shaped `[batch_size, alignments_size]`
          (`alignments_size` is the values' `max_time`).
        """
        max_time = self._alignments_size
        return array_ops.one_hot(
            array_ops.zeros((batch_size,), dtype=dtypes.int32), max_time,
            dtype=dtype)


class BahdanauMonotonicAttention(_BaseMonotonicAttentionMechanism):
    """Monotonic attention mechanism with Bahadanau-style energy function.

    This type of attention encorces a monotonic constraint on the attention
    distributions; that is once the model attends to a given point in the memory
    it can't attend to any prior points at subsequence output timesteps.  It
    achieves this by using the _monotonic_probability_fn instead of softmax to
    construct its attention distributions.  Since the attention scores are passed
    through a sigmoid, a learnable scalar bias parameter is applied after the
    score function and before the sigmoid.  Otherwise, it is equivalent to
    BahdanauAttention.  This approach is proposed in

    Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck,
    "Online and Linear-Time Attention by Enforcing Monotonic Alignments."
    ICML 2017.  https://arxiv.org/abs/1704.00784
    """

    def __init__(self,
                 num_units,
                 memory,
                 memory_sequence_length=None,
                 normalize=False,
                 score_mask_value=None,
                 sigmoid_noise=0.,
                 sigmoid_noise_seed=None,
                 score_bias_init=0.,
                 mode="parallel",
                 dtype=None,
                 name="BahdanauMonotonicAttention"):
        """Construct the Attention mechanism.

        Args:
          num_units: The depth of the query mechanism.
          memory: The memory to query; usually the output of an RNN encoder.  This
            tensor should be shaped `[batch_size, max_time, ...]`.
          memory_sequence_length (optional): Sequence lengths for the batch entries
            in memory.  If provided, the memory tensor rows are masked with zeros
            for values past the respective sequence lengths.
          normalize: Python boolean.  Whether to normalize the energy term.
          score_mask_value: (optional): The mask value for score before passing into
            `probability_fn`. The default is -inf. Only used if
            `memory_sequence_length` is not None.
          sigmoid_noise: Standard deviation of pre-sigmoid noise.  See the docstring
            for `_monotonic_probability_fn` for more information.
          sigmoid_noise_seed: (optional) Random seed for pre-sigmoid noise.
          score_bias_init: Initial value for score bias scalar.  It's recommended to
            initialize this to a negative value when the length of the memory is
            large.
          mode: How to compute the attention distribution.  Must be one of
            'recursive', 'parallel', or 'hard'.  See the docstring for
            `tf.contrib.seq2seq.monotonic_attention` for more information.
          dtype: The data type for the query and memory layers of the attention
            mechanism.
          name: Name to use when creating ops.
        """
        # Set up the monotonic probability fn with supplied parameters
        if dtype is None:
            dtype = dtypes.float32
        wrapped_probability_fn = functools.partial(
            _monotonic_probability_fn, sigmoid_noise=sigmoid_noise, mode=mode,
            seed=sigmoid_noise_seed)
        super(BahdanauMonotonicAttention, self).__init__(
            query_layer=layers_core.Dense(
                num_units, name="query_layer", use_bias=False, dtype=dtype),
            memory_layer=layers_core.Dense(
                num_units, name="memory_layer", use_bias=False, dtype=dtype),
            memory=memory,
            probability_fn=wrapped_probability_fn,
            memory_sequence_length=memory_sequence_length,
            score_mask_value=score_mask_value,
            name=name)
        self._num_units = num_units
        self._normalize = normalize
        self._name = name
        self._score_bias_init = score_bias_init

    def __call__(self, query, previous_alignments):
        """Score the query based on the keys and values.

        Args:
          query: Tensor of dtype matching `self.values` and shape
            `[batch_size, query_depth]`.
          previous_alignments: Tensor of dtype matching `self.values` and shape
            `[batch_size, alignments_size]`
            (`alignments_size` is memory's `max_time`).

        Returns:
          alignments: Tensor of dtype matching `self.values` and shape
            `[batch_size, alignments_size]` (`alignments_size` is memory's
            `max_time`).
        """
        with variable_scope.variable_scope(
                None, "bahdanau_monotonic_attention", [query]):
            processed_query = self.query_layer(query) if self.query_layer else query
            score = _bahdanau_score(processed_query, self._keys, self._normalize)
            score_bias = variable_scope.get_variable(
                "attention_score_bias", dtype=processed_query.dtype,
                initializer=self._score_bias_init)
            score += score_bias
        alignments = self._probability_fn(score, previous_alignments)
        return alignments


class LuongMonotonicAttention(_BaseMonotonicAttentionMechanism):
    """Monotonic attention mechanism with Luong-style energy function.

    This type of attention encorces a monotonic constraint on the attention
    distributions; that is once the model attends to a given point in the memory
    it can't attend to any prior points at subsequence output timesteps.  It
    achieves this by using the _monotonic_probability_fn instead of softmax to
    construct its attention distributions.  Otherwise, it is equivalent to
    LuongAttention.  This approach is proposed in

    Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck,
    "Online and Linear-Time Attention by Enforcing Monotonic Alignments."
    ICML 2017.  https://arxiv.org/abs/1704.00784
    """

    def __init__(self,
                 num_units,
                 memory,
                 memory_sequence_length=None,
                 scale=False,
                 score_mask_value=None,
                 sigmoid_noise=0.,
                 sigmoid_noise_seed=None,
                 score_bias_init=0.,
                 mode="parallel",
                 dtype=None,
                 name="LuongMonotonicAttention"):
        """Construct the Attention mechanism.

        Args:
          num_units: The depth of the query mechanism.
          memory: The memory to query; usually the output of an RNN encoder.  This
            tensor should be shaped `[batch_size, max_time, ...]`.
          memory_sequence_length (optional): Sequence lengths for the batch entries
            in memory.  If provided, the memory tensor rows are masked with zeros
            for values past the respective sequence lengths.
          scale: Python boolean.  Whether to scale the energy term.
          score_mask_value: (optional): The mask value for score before passing into
            `probability_fn`. The default is -inf. Only used if
            `memory_sequence_length` is not None.
          sigmoid_noise: Standard deviation of pre-sigmoid noise.  See the docstring
            for `_monotonic_probability_fn` for more information.
          sigmoid_noise_seed: (optional) Random seed for pre-sigmoid noise.
          score_bias_init: Initial value for score bias scalar.  It's recommended to
            initialize this to a negative value when the length of the memory is
            large.
          mode: How to compute the attention distribution.  Must be one of
            'recursive', 'parallel', or 'hard'.  See the docstring for
            `tf.contrib.seq2seq.monotonic_attention` for more information.
          dtype: The data type for the query and memory layers of the attention
            mechanism.
          name: Name to use when creating ops.
        """
        # Set up the monotonic probability fn with supplied parameters
        if dtype is None:
            dtype = dtypes.float32
        wrapped_probability_fn = functools.partial(
            _monotonic_probability_fn, sigmoid_noise=sigmoid_noise, mode=mode,
            seed=sigmoid_noise_seed)
        super(LuongMonotonicAttention, self).__init__(
            query_layer=layers_core.Dense(
                num_units, name="query_layer", use_bias=False, dtype=dtype),
            memory_layer=layers_core.Dense(
                num_units, name="memory_layer", use_bias=False, dtype=dtype),
            memory=memory,
            probability_fn=wrapped_probability_fn,
            memory_sequence_length=memory_sequence_length,
            score_mask_value=score_mask_value,
            name=name)
        self._num_units = num_units
        self._scale = scale
        self._score_bias_init = score_bias_init
        self._name = name

    def __call__(self, query, previous_alignments):
        """Score the query based on the keys and values.

        Args:
          query: Tensor of dtype matching `self.values` and shape
            `[batch_size, query_depth]`.
          previous_alignments: Tensor of dtype matching `self.values` and shape
            `[batch_size, alignments_size]`
            (`alignments_size` is memory's `max_time`).

        Returns:
          alignments: Tensor of dtype matching `self.values` and shape
            `[batch_size, alignments_size]` (`alignments_size` is memory's
            `max_time`).
        """
        with variable_scope.variable_scope(None, "luong_monotonic_attention",
                                           [query]):
            score = _luong_score(query, self._keys, self._scale)
            score_bias = variable_scope.get_variable(
                "attention_score_bias", dtype=query.dtype,
                initializer=self._score_bias_init)
            score += score_bias
        alignments = self._probability_fn(score, previous_alignments)
        return alignments


class AttentionWrapperState(
    collections.namedtuple("AttentionWrapperState",
                           ("cell_state", "attention", "time", "alignments",
                            "alignment_history"))):
    """`namedtuple` storing the state of a `AttentionWrapper`.

    Contains:

      - `cell_state`: The state of the wrapped `RNNCell` at the previous time
        step.
      - `attention`: The attention emitted at the previous time step.
      - `time`: int32 scalar containing the current time step.
      - `alignments`: A single or tuple of `Tensor`(s) containing the alignments
         emitted at the previous time step for each attention mechanism.
      - `alignment_history`: (if enabled) a single or tuple of `TensorArray`(s)
         containing alignment matrices from all time steps for each attention
         mechanism. Call `stack()` on each to convert to a `Tensor`.
    """

    def clone(self, **kwargs):
        """Clone this object, overriding components provided by kwargs.

        Example:

        ```python
        initial_state = attention_wrapper.zero_state(dtype=..., batch_size=...)
        initial_state = initial_state.clone(cell_state=encoder_state)
        ```

        Args:
          **kwargs: Any properties of the state object to replace in the returned
            `AttentionWrapperState`.

        Returns:
          A new `AttentionWrapperState` whose properties are the same as
          this one, except any overridden properties as provided in `kwargs`.
        """
        return super(AttentionWrapperState, self)._replace(**kwargs)


def hardmax(logits, name=None):
    """Returns batched one-hot vectors.

    The depth index containing the `1` is that of the maximum logit value.

    Args:
      logits: A batch tensor of logit values.
      name: Name to use when creating ops.
    Returns:
      A batched one-hot tensor.
    """
    with ops.name_scope(name, "Hardmax", [logits]):
        logits = ops.convert_to_tensor(logits, name="logits")
        if logits.get_shape()[-1].value is not None:
            depth = logits.get_shape()[-1].value
        else:
            depth = array_ops.shape(logits)[-1]
        return array_ops.one_hot(
            math_ops.argmax(logits, -1), depth, dtype=logits.dtype)


def _compute_attention(attention_mechanism, cell_output, previous_alignments,
                       attention_layer, temperature, use_hmean):
    """Computes the attention and alignments for a given attention_mechanism."""
    alignments = attention_mechanism(
        cell_output, previous_alignments=previous_alignments)

    # Reshape from [batch_size, memory_time] to [batch_size, 1, memory_time]
    expanded_alignments = array_ops.expand_dims(alignments, 1)
    # Context is the inner product of alignments and values along the
    # memory time dimension.
    # alignments shape is
    #   [batch_size, 1, memory_time]
    # attention_mechanism.values shape is
    #   [batch_size, memory_time, memory_size]
    # the batched matmul is over memory_time, so the output shape is
    #   [batch_size, 1, memory_size].
    # we then squeeze out the singleton dim.
    context = math_ops.matmul(expanded_alignments, attention_mechanism.values)
    context = array_ops.squeeze(context, [1])

    ## Get context vector mean and log standard deviation
    c_dim = context.get_shape()[-1]  # The dimension of the context vector
    c_mean = tf.identity(context, name='c_mean')
    c_log_sigma_intermediate = Dense(c_dim, activation=tf.tanh, name='c_log_sigma_intermediate')(context)
    c_log_sigma = Dense(c_dim, name='c_log_sigma')(c_log_sigma_intermediate)

    ## Sample from the gaussian distribution
    epsilon = tf.random_normal(tf.shape(c_log_sigma), name="epsilon")
    context_sampled = c_mean + tf.scalar_mul(temperature, epsilon * tf.exp(c_log_sigma))

    if use_hmean:
        ## Prior mean is mean pooling of encoder outputs: Take sum and divide by num of unmasked tokens
        h_source_mean = tf.reduce_sum(attention_mechanism.values, axis=1) / tf.cast(
            tf.count_nonzero(tf.reduce_sum(attention_mechanism.values, axis=-1), axis=1, keep_dims=True),
            dtype=tf.float32)

        ## Calculate KL Loss for the context vector for h_mean prior
        c_kl_loss = -0.5 * tf.reduce_sum(1.0 + 2 * c_log_sigma - (c_mean - h_source_mean) ** 2 - tf.exp(2 * c_log_sigma), 1,
                                         name="c_kl_loss")
    else:
        ## Calculate KL Loss for the context vector for zero prior
        c_kl_loss = -0.5 * tf.reduce_sum(
            1.0 + 2 * c_log_sigma - (c_mean) ** 2 - tf.exp(2 * c_log_sigma), 1,
            name="c_kl_loss")

    # Alternative KL loss calculation b/w two gaussian distributions
    # prior_mean = tf.zeros(shape=c_mean.shape)
    # prior_sigma = tf.ones(shape=c_mean.shape)
    # posterior_dist = tf.contrib.distributions.Normal(loc=c_mean, scale=tf.exp(c_log_sigma))
    # prior_dist = tf.contrib.distributions.Normal(loc=prior_mean, scale=prior_sigma)
    # c_kl_loss = tf.reduce_sum(tf.contrib.distributions.kl_divergence(posterior_dist, prior_dist), axis=-1, name="c_kl_loss")

    if attention_layer is not None:
        attention = attention_layer(array_ops.concat([cell_output, context_sampled], 1))
    else:
        attention = context_sampled

    return attention, alignments, c_kl_loss


class AttentionWrapper(rnn_cell_impl.RNNCell):
    """Wraps another `RNNCell` with attention.
    """

    def __init__(self,
                 cell,
                 attention_mechanism,
                 temperature=1.0,
                 use_hmean = True,
                 attention_layer_size=None,
                 alignment_history=False,
                 cell_input_fn=None,
                 output_attention=True,
                 initial_cell_state=None,
                 name=None):
        """Construct the `AttentionWrapper`.

        **NOTE** If you are using the `BeamSearchDecoder` with a cell wrapped in
        `AttentionWrapper`, then you must ensure that:

        - The encoder output has been tiled to `beam_width` via
          @{tf.contrib.seq2seq.tile_batch} (NOT `tf.tile`).
        - The `batch_size` argument passed to the `zero_state` method of this
          wrapper is equal to `true_batch_size * beam_width`.
        - The initial state created with `zero_state` above contains a
          `cell_state` value containing properly tiled final state from the
          encoder.

        An example:

        ```
        tiled_encoder_outputs = tf.contrib.seq2seq.tile_batch(
            encoder_outputs, multiplier=beam_width)
        tiled_encoder_final_state = tf.conrib.seq2seq.tile_batch(
            encoder_final_state, multiplier=beam_width)
        tiled_sequence_length = tf.contrib.seq2seq.tile_batch(
            sequence_length, multiplier=beam_width)
        attention_mechanism = MyFavoriteAttentionMechanism(
            num_units=attention_depth,
            memory=tiled_inputs,
            memory_sequence_length=tiled_sequence_length)
        attention_cell = AttentionWrapper(cell, attention_mechanism, ...)
        decoder_initial_state = attention_cell.zero_state(
            dtype, batch_size=true_batch_size * beam_width)
        decoder_initial_state = decoder_initial_state.clone(
            cell_state=tiled_encoder_final_state)
        ```

        Args:
          cell: An instance of `RNNCell`.
          attention_mechanism: A list of `AttentionMechanism` instances or a single
            instance.
          attention_layer_size: A list of Python integers or a single Python
            integer, the depth of the attention (output) layer(s). If None
            (default), use the context as attention at each time step. Otherwise,
            feed the context and cell output into the attention layer to generate
            attention at each time step. If attention_mechanism is a list,
            attention_layer_size must be a list of the same length.
          alignment_history: Python boolean, whether to store alignment history
            from all time steps in the final output state (currently stored as a
            time major `TensorArray` on which you must call `stack()`).
          cell_input_fn: (optional) A `callable`.  The default is:
            `lambda inputs, attention: array_ops.concat([inputs, attention], -1)`.
          output_attention: Python bool.  If `True` (default), the output at each
            time step is the attention value.  This is the behavior of Luong-style
            attention mechanisms.  If `False`, the output at each time step is
            the output of `cell`.  This is the beahvior of Bhadanau-style
            attention mechanisms.  In both cases, the `attention` tensor is
            propagated to the next time step via the state and is used there.
            This flag only controls whether the attention mechanism is propagated
            up to the next cell in an RNN stack or to the top RNN output.
          initial_cell_state: The initial state value to use for the cell when
            the user calls `zero_state()`.  Note that if this value is provided
            now, and the user uses a `batch_size` argument of `zero_state` which
            does not match the batch size of `initial_cell_state`, proper
            behavior is not guaranteed.
          name: Name to use when creating ops.

        Raises:
          TypeError: `attention_layer_size` is not None and (`attention_mechanism`
            is a list but `attention_layer_size` is not; or vice versa).
          ValueError: if `attention_layer_size` is not None, `attention_mechanism`
            is a list, and its length does not match that of `attention_layer_size`.
        """

        super(AttentionWrapper, self).__init__(name=name)
        if isinstance(attention_mechanism, (list, tuple)):
            self._is_multi = True
            attention_mechanisms = attention_mechanism
            for attention_mechanism in attention_mechanisms:
                if not isinstance(attention_mechanism, AttentionMechanism):
                    raise TypeError(
                        "attention_mechanism must contain only instances of "
                        "AttentionMechanism, saw type: %s"
                        % type(attention_mechanism).__name__)
        else:
            self._is_multi = False
            if not isinstance(attention_mechanism, AttentionMechanism):
                raise TypeError(
                    "attention_mechanism must be an AttentionMechanism or list of "
                    "multiple AttentionMechanism instances, saw type: %s"
                    % type(attention_mechanism).__name__)
            attention_mechanisms = (attention_mechanism,)

        if cell_input_fn is None:
            cell_input_fn = (
                lambda inputs, attention: array_ops.concat([inputs, attention], -1))
        else:
            if not callable(cell_input_fn):
                raise TypeError(
                    "cell_input_fn must be callable, saw type: %s"
                    % type(cell_input_fn).__name__)

        if attention_layer_size is not None:
            attention_layer_sizes = tuple(
                attention_layer_size
                if isinstance(attention_layer_size, (list, tuple))
                else (attention_layer_size,))
            if len(attention_layer_sizes) != len(attention_mechanisms):
                raise ValueError(
                    "If provided, attention_layer_size must contain exactly one "
                    "integer per attention_mechanism, saw: %d vs %d"
                    % (len(attention_layer_sizes), len(attention_mechanisms)))
            self._attention_layers = tuple(
                layers_core.Dense(
                    attention_layer_size,
                    name="attention_layer",
                    use_bias=False,
                    dtype=attention_mechanisms[i].dtype)
                for i, attention_layer_size in enumerate(attention_layer_sizes))
            self._attention_layer_size = sum(attention_layer_sizes)
        else:
            self._attention_layers = None
            self._attention_layer_size = sum(
                attention_mechanism.values.get_shape()[-1].value
                for attention_mechanism in attention_mechanisms)

        self._cell = cell
        self._attention_mechanisms = attention_mechanisms
        self._cell_input_fn = cell_input_fn
        self._output_attention = output_attention
        self._alignment_history = alignment_history
        self._temperature = temperature
        self._use_hmean = use_hmean # Use N(h_mean_src, I) as prior instead of N(0, I)
        with ops.name_scope(name, "AttentionWrapperInit"):
            if initial_cell_state is None:
                self._initial_cell_state = None
            else:
                final_state_tensor = nest.flatten(initial_cell_state)[-1]
                state_batch_size = (
                        final_state_tensor.shape[0].value
                        or array_ops.shape(final_state_tensor)[0])
                error_message = (
                        "When constructing AttentionWrapper %s: " % self._base_name +
                        "Non-matching batch sizes between the memory "
                        "(encoder output) and initial_cell_state.  Are you using "
                        "the BeamSearchDecoder?  You may need to tile your initial state "
                        "via the tf.contrib.seq2seq.tile_batch function with argument "
                        "multiple=beam_width.")
                with ops.control_dependencies(
                        self._batch_size_checks(state_batch_size, error_message)):
                    self._initial_cell_state = nest.map_structure(
                        lambda s: array_ops.identity(s, name="check_initial_cell_state"),
                        initial_cell_state)

    def _batch_size_checks(self, batch_size, error_message):
        return [check_ops.assert_equal(batch_size,
                                       attention_mechanism.batch_size,
                                       message=error_message)
                for attention_mechanism in self._attention_mechanisms]

    def _item_or_tuple(self, seq):
        """Returns `seq` as tuple or the singular element.

        Which is returned is determined by how the AttentionMechanism(s) were passed
        to the constructor.

        Args:
          seq: A non-empty sequence of items or generator.

        Returns:
           Either the values in the sequence as a tuple if AttentionMechanism(s)
           were passed to the constructor as a sequence or the singular element.
        """
        t = tuple(seq)
        if self._is_multi:
            return t
        else:
            return t[0]

    @property
    def output_size(self):
        if self._output_attention:
            return self._attention_layer_size
        else:
            return self._cell.output_size

    @property
    def state_size(self):
        """The `state_size` property of `AttentionWrapper`.

        Returns:
          An `AttentionWrapperState` tuple containing shapes used by this object.
        """
        return AttentionWrapperState(
            cell_state=self._cell.state_size,
            time=tensor_shape.TensorShape([]),
            attention=self._attention_layer_size,
            alignments=self._item_or_tuple(
                a.alignments_size for a in self._attention_mechanisms),
            alignment_history=self._item_or_tuple(
                () for _ in self._attention_mechanisms))  # sometimes a TensorArray

    def zero_state(self, batch_size, dtype):
        """Return an initial (zero) state tuple for this `AttentionWrapper`.

        **NOTE** Please see the initializer documentation for details of how
        to call `zero_state` if using an `AttentionWrapper` with a
        `BeamSearchDecoder`.

        Args:
          batch_size: `0D` integer tensor: the batch size.
          dtype: The internal state data type.

        Returns:
          An `AttentionWrapperState` tuple containing zeroed out tensors and,
          possibly, empty `TensorArray` objects.

        Raises:
          ValueError: (or, possibly at runtime, InvalidArgument), if
            `batch_size` does not match the output size of the encoder passed
            to the wrapper object at initialization time.
        """
        with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
            if self._initial_cell_state is not None:
                cell_state = self._initial_cell_state
            else:
                cell_state = self._cell.zero_state(batch_size, dtype)
            error_message = (
                    "When calling zero_state of AttentionWrapper %s: " % self._base_name +
                    "Non-matching batch sizes between the memory "
                    "(encoder output) and the requested batch size.  Are you using "
                    "the BeamSearchDecoder?  If so, make sure your encoder output has "
                    "been tiled to beam_width via tf.contrib.seq2seq.tile_batch, and "
                    "the batch_size= argument passed to zero_state is "
                    "batch_size * beam_width.")
            with ops.control_dependencies(
                    self._batch_size_checks(batch_size, error_message)):
                cell_state = nest.map_structure(
                    lambda s: array_ops.identity(s, name="checked_cell_state"),
                    cell_state)
            return AttentionWrapperState(
                cell_state=cell_state,
                time=array_ops.zeros([], dtype=dtypes.int32),
                attention=_zero_state_tensors(self._attention_layer_size, batch_size,
                                              dtype),
                alignments=self._item_or_tuple(
                    attention_mechanism.initial_alignments(batch_size, dtype)
                    for attention_mechanism in self._attention_mechanisms),
                alignment_history=self._item_or_tuple(
                    tensor_array_ops.TensorArray(dtype=dtype, size=0,
                                                 dynamic_size=True)
                    if self._alignment_history else ()
                    for _ in self._attention_mechanisms))

    def call(self, inputs, state):
        """Perform a step of attention-wrapped RNN.

        - Step 1: Mix the `inputs` and previous step's `attention` output via
          `cell_input_fn`.
        - Step 2: Call the wrapped `cell` with this input and its previous state.
        - Step 3: Score the cell's output with `attention_mechanism`.
        - Step 4: Calculate the alignments by passing the score through the
          `normalizer`.
        - Step 5: Calculate the context vector as the inner product between the
          alignments and the attention_mechanism's values (memory).
        - Step 6: Calculate the attention output by concatenating the cell output
          and context through the attention layer (a linear layer with
          `attention_layer_size` outputs).

        Args:
          inputs: (Possibly nested tuple of) Tensor, the input at this time step.
          state: An instance of `AttentionWrapperState` containing
            tensors from the previous time step.

        Returns:
          A tuple `(attention_or_cell_output, next_state)`, where:

          - `attention_or_cell_output` depending on `output_attention`.
          - `next_state` is an instance of `AttentionWrapperState`
             containing the state calculated at this time step.

        Raises:
          TypeError: If `state` is not an instance of `AttentionWrapperState`.
        """
        if not isinstance(state, AttentionWrapperState):
            raise TypeError("Expected state to be instance of AttentionWrapperState. "
                            "Received type %s instead." % type(state))

        # Step 1: Calculate the true inputs to the cell based on the
        # previous attention value.
        cell_inputs = self._cell_input_fn(inputs, state.attention)
        cell_state = state.cell_state
        cell_output, next_cell_state = self._cell(cell_inputs, cell_state)

        cell_batch_size = (
                cell_output.shape[0].value or array_ops.shape(cell_output)[0])
        error_message = (
                "When applying AttentionWrapper %s: " % self.name +
                "Non-matching batch sizes between the memory "
                "(encoder output) and the query (decoder output).  Are you using "
                "the BeamSearchDecoder?  You may need to tile your memory input via "
                "the tf.contrib.seq2seq.tile_batch function with argument "
                "multiple=beam_width.")
        with ops.control_dependencies(
                self._batch_size_checks(cell_batch_size, error_message)):
            cell_output = array_ops.identity(
                cell_output, name="checked_cell_output")

        if self._is_multi:
            previous_alignments = state.alignments
            previous_alignment_history = state.alignment_history
        else:
            previous_alignments = [state.alignments]
            previous_alignment_history = [state.alignment_history]

        all_alignments = []
        all_attentions = []
        all_histories = []
        ## Obtain c_kl_loss (for the current timestep of decoder)
        for i, attention_mechanism in enumerate(self._attention_mechanisms):
            attention, alignments, c_kl_loss = _compute_attention(
                attention_mechanism, cell_output, previous_alignments[i],
                self._attention_layers[i] if self._attention_layers else None, self._temperature, self._use_hmean)
            alignment_history = previous_alignment_history[i].write(
                state.time, alignments) if self._alignment_history else ()

            all_alignments.append(alignments)
            all_histories.append(alignment_history)
            all_attentions.append(attention)

        attention = array_ops.concat(all_attentions, 1)
        next_state = AttentionWrapperState(
            time=state.time + 1,
            cell_state=next_cell_state,
            attention=attention,
            alignments=self._item_or_tuple(all_alignments),
            alignment_history=self._item_or_tuple(all_histories))

        if self._output_attention:
            return attention, next_state, c_kl_loss
        else:
            return cell_output, next_state, c_kl_loss
